Tubi Data Engineer Interview Questions + Guide in 2025

Tubi is a leading ad-supported video-on-demand streaming service that provides a vast array of free entertainment options, including movies, TV shows, and exclusive originals, to audiences across the U.S. and Canada.

As a Data Engineer at Tubi, you will play a crucial role in building and maintaining the data infrastructure that supports the company's extensive content catalog and user engagement. Your responsibilities will include designing and implementing scalable data pipelines, ensuring data quality, and enabling seamless data access for stakeholders. You will collaborate closely with cross-functional teams, including data scientists and product managers, to facilitate data-driven decision-making. The ideal candidate will possess a strong foundation in big data technologies, familiarity with cloud computing platforms, and a passion for optimizing data processes to enhance product offerings. At Tubi, you will contribute to a culture that values creativity, innovation, and the pursuit of excellence in data engineering.

This guide will equip you with insights and tailored preparation strategies to excel in your interview for the Data Engineer role at Tubi, ensuring you can effectively demonstrate your skills and fit for the company.

The interview process for a Data Engineer role at Tubi is structured to assess both technical skills and cultural fit within the company. It typically consists of several key stages:

1. Initial Recruiter Screen

The process begins with a phone call from a recruiter, lasting about 30 minutes. During this conversation, the recruiter will discuss your background, relevant experience, and motivations for applying to Tubi. This is also an opportunity for you to learn more about the company culture and the specifics of the role.

2. Hiring Manager Interview

Following the recruiter screen, candidates usually have a one-on-one interview with the hiring manager. This discussion often revolves around your past experiences, particularly focusing on specific projects you've led or contributed to. Expect to explain your technical skills and how they align with Tubi's needs. In some cases, this interview may include a technical assessment, such as a whiteboarding exercise or a discussion of programmatic server systems.

3. Technical Assessment

Candidates may be required to complete a technical assessment, which could be a take-home test or a live coding session. This assessment typically includes SQL questions, data manipulation tasks, and possibly a case study related to data engineering challenges. The goal is to evaluate your problem-solving abilities and technical proficiency in handling large datasets.

4. Onsite Interview

The final stage is an onsite interview, which may be conducted virtually. This round usually consists of multiple interviews with various team members, including engineers and data scientists. Each interview lasts around 30 minutes and covers a range of topics, including data pipeline design, data quality management, and your approach to collaboration with cross-functional teams. Be prepared for both technical questions and behavioral inquiries that assess your fit within Tubi's culture.

Throughout the process, candidates should be ready to discuss their understanding of Tubi's data needs and how they can contribute to the company's mission of providing accessible entertainment.

Next, let's delve into the specific interview questions that candidates have encountered during this process.

Here are some tips to help you excel in your interview.

Understand the Data Landscape at Tubi

Before your interview, familiarize yourself with Tubi's data ecosystem. Given the company's emphasis on data-driven decision-making, understanding how Tubi collects, processes, and utilizes data will be crucial. Research their data platforms, the types of data they handle, and how they leverage machine learning to enhance user experience. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the role and the company.

Prepare for Technical Assessments

Expect to encounter technical assessments that may include SQL queries, data pipeline design, and possibly a take-home test. Brush up on your SQL skills, particularly with complex queries involving joins, aggregations, and window functions. Additionally, be prepared to discuss your experience with big data technologies like Spark, AWS, and any relevant ETL processes. Practicing real-world scenarios where you've built or optimized data pipelines will give you a solid foundation to draw from during the interview.

Showcase Problem-Solving Skills

Tubi values engineers who can tackle complex problems. Be ready to discuss specific challenges you've faced in previous roles and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on the impact of your solutions. Highlight your ability to work collaboratively with cross-functional teams, as this is essential for the role.

Communicate Clearly and Confidently

During the interview, clear communication is key. Be concise and articulate when discussing your past experiences and technical knowledge. Avoid jargon unless necessary, and ensure that your explanations are accessible to non-technical interviewers. This will demonstrate your ability to bridge the gap between technical and non-technical stakeholders, a valuable skill in a data engineering role.

Emphasize Adaptability and Continuous Learning

Given the fast-paced nature of Tubi's environment, showcasing your adaptability and willingness to learn is vital. Share examples of how you've quickly adapted to new technologies or processes in the past. Discuss any recent projects or courses you've undertaken to stay current in the field, particularly in areas like cloud technologies or data engineering best practices.

Prepare for Behavioral Questions

Expect behavioral questions that assess your fit within Tubi's culture. Reflect on your past experiences and how they align with Tubi's values, such as creativity, self-motivation, and teamwork. Be prepared to discuss how you've contributed to a positive team culture and how you handle challenges or conflicts in a collaborative setting.

Follow Up Thoughtfully

After your interview, send a thoughtful follow-up email to express your gratitude for the opportunity and reiterate your enthusiasm for the role. Mention specific topics discussed during the interview that resonated with you, reinforcing your interest in contributing to Tubi's mission. This not only shows professionalism but also keeps you top of mind as they make their decision.

By following these tips, you'll be well-prepared to navigate the interview process at Tubi and demonstrate your potential as a valuable addition to their data engineering team. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Tubi. The interview process will likely focus on your technical skills, experience with data pipelines, and your ability to work with large datasets. Be prepared to discuss your past projects, the technologies you've used, and how you approach problem-solving in data engineering.

Technical Skills

1. Can you describe your experience with building scalable data pipelines?

This question aims to assess your hands-on experience and understanding of data pipeline architecture.

How to Answer

Discuss specific projects where you built data pipelines, the technologies you used (like Spark or Flink), and the challenges you faced. Highlight how you ensured scalability and reliability in your solutions.

Example

“In my previous role, I designed a data pipeline using Apache Spark that processed terabytes of data daily. I implemented partitioning strategies to optimize performance and used AWS S3 for storage, which allowed for seamless scaling as our data volume grew.”

2. What strategies do you use to ensure data quality in your pipelines?

This question evaluates your approach to maintaining data integrity and quality.

How to Answer

Explain the methods you employ to monitor and validate data quality, such as automated testing, data profiling, and implementing alerts for anomalies.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Great Expectations to profile incoming data. Additionally, I set up alerts for any discrepancies, allowing us to address issues proactively before they affect downstream processes.”

3. How do you handle data ingestion from multiple sources?

This question assesses your experience with ETL processes and data integration.

How to Answer

Discuss your experience with different data sources, the tools you’ve used for ingestion, and how you manage schema changes or data format variations.

Example

“I’ve worked with various data sources, including APIs and databases. I typically use Apache NiFi for data ingestion, which allows me to easily handle different formats and automate the ETL process. I also ensure that I have a robust schema management strategy in place to accommodate changes.”

4. Can you explain the difference between batch processing and stream processing?

This question tests your understanding of fundamental data processing concepts.

How to Answer

Clearly define both concepts and provide examples of when you would use each type of processing.

Example

“Batch processing involves processing large volumes of data at once, typically on a scheduled basis, while stream processing handles data in real-time as it arrives. For instance, I would use batch processing for monthly reporting, but stream processing for real-time analytics on user interactions.”

5. What tools and technologies do you prefer for data transformation?

This question gauges your familiarity with data transformation tools and your preferences.

How to Answer

Mention specific tools you have experience with, such as DBT or Apache Spark, and explain why you prefer them.

Example

“I prefer using DBT for data transformation because it allows for modular SQL development and easy testing of transformations. It integrates well with our data warehouse and helps maintain a clear version control of our transformation logic.”

Behavioral Questions

1. Describe a challenging data engineering problem you faced and how you solved it.

This question looks for your problem-solving skills and resilience.

How to Answer

Share a specific example, detailing the problem, your approach to solving it, and the outcome.

Example

“Once, I faced a significant performance issue with a data pipeline that was causing delays in reporting. I conducted a thorough analysis and discovered that the bottleneck was due to inefficient joins in our SQL queries. I optimized the queries and implemented indexing, which improved the performance by over 50%.”

2. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use to manage your workload.

Example

“I prioritize tasks based on their impact and urgency. I use a Kanban board to visualize my workload and ensure that I’m focusing on high-impact tasks first. Regular check-ins with my team also help me stay aligned with project goals.”

3. How do you ensure effective communication with non-technical stakeholders?

This question evaluates your communication skills and ability to bridge the gap between technical and non-technical teams.

How to Answer

Explain your approach to simplifying complex technical concepts and ensuring that stakeholders understand the implications of data-related decisions.

Example

“I focus on using clear, non-technical language and visual aids, such as dashboards or flowcharts, to explain data processes. I also encourage questions to ensure that everyone is on the same page and understands the data’s impact on their work.”

4. Can you give an example of how you mentored a junior engineer?

This question assesses your leadership and mentoring abilities.

How to Answer

Share a specific instance where you provided guidance or support to a junior engineer, detailing the context and outcome.

Example

“I mentored a junior engineer who was struggling with SQL queries. I organized a series of pair programming sessions where we worked through real-world problems together. By the end of the project, they were able to write complex queries independently and even contributed to optimizing our existing queries.”

5. What motivates you to work in data engineering?

This question seeks to understand your passion and commitment to the field.

How to Answer

Discuss what excites you about data engineering, such as solving complex problems, working with large datasets, or the impact of data-driven decisions.

Example

“I’m motivated by the challenge of transforming raw data into actionable insights. I find it rewarding to build systems that empower teams to make data-driven decisions, ultimately contributing to the success of the organization.”